Autoencoder Based Noise Cancellation for Real-Time Audio Enhancement

Real-time audio enhancement, particularly for noise cancellation, is critical in applications like telecommunication, speech recognition, and hearing aids. Traditional techniques like spectral subtraction and Wiener filtering often struggle to preserve audio quality while effectively reducing noise. These methods, while useful in noise suppression, can sometimes lead to artifacts or distortions in the audio, making it difficult to achieve a balance between noise removal and audio clarity. This paper proposes a novel solution using an autoencoder based architecture for real-time noise cancellation. Autoencoders, a type of deep neural network, are well-suited for learning efficient representations of noisy audio and reconstructing clean signals in an unsupervised manner. In this work, we introduce a convolutional autoencoder (CAE)-based system that denoises audio signals in real-time. The system is trained on noisy and clean audio pairs, where the encoder extracts essential features from the noisy input, and the decoder reconstructs the clean output. To ensure real-time performance, the architecture is designed to be lightweight and optimized for speed. We evaluate the method across various noisy audio datasets, including both stationary and non-stationary noise. The results demonstrate that the autoencoder-based system outperforms traditional methods in both noise reduction and perceptual audio quality. It also exhibits robustness in dynamic real-time environments, with low latency and minimal computational cost, making it suit able for integration into consumer devices and communication systems. This research highlights the potential of deep learning approaches, especially autoencoder, in advancing real-time audio enhancement, offering a promising solution for high-quality noise cancellation in diverse and dynamic settings.